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We propose and evaluate a learning-based framework to address multi-agent resource allocation in coupled wireless systems. In particular we consider, multiple agents (e.g., base stations, access points, etc.) that choose amongst a set of resource allocation options towards achieving their own performance objective /requirements, and where the performance observed at each agent is further coupled with the actions chosen by the other agents, e.g., through interference, channel leakage, etc. The challenge is to find the best collective action. To that end we propose a Multi-Armed Bandit (MAB) framework wherein the best actions (aka arms) are adaptively learned through online reward feedback. Our focus is on systems which are "weakly-coupled" wherein the best arm of each agent is invariant to others' arm selection the majority of the time - this majority structure enables one to develop light weight efficient algorithms. This structure is commonly found in many wireless settings such as channel selection and power control. We develop a bandit algorithm based on the Track-and-Stop strategy, which shows a logarithmic regret with respect to a genie. Finally through simulation, we exhibit the potential use of our model and algorithm in several wireless application scenarios.
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Covert Best Arm Identification of Stochastic Bandits
We study the covert best arm identification problem in which an agent tries to identify the best arm while escaping detection from an adversary. Specifically, the agent should identify the best arm of the bandit with accuracy higher than a predefined requirement as soon as possible and, simultaneously, the adversary’s observations induced by pulling effective arms should remain indistinguishable from the observations obtained when no effective arm is pulled. Our main result is the characterization of the exponent γ, which captures the asymptotic exponential decrease of the confidence level with the square-root of the averaged stopping time.
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- Award ID(s):
- 1955401
- PAR ID:
- 10413180
- Date Published:
- Journal Name:
- Proc. of IEEE International Symposium on Information Theory
- Page Range / eLocation ID:
- 324 to 329
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISIT50566.2022.9834559
